Senior Data Scientist, Data Quality and AI Evaluation

At NVIDIA, we are building the next generation AI-native autonomous driving architecture — combining classical safety stacks, foundation models, and scalable AI systems into a unified production platform. Autonomous driving is entering a new era. Future AV systems will not be purely classical robotics stacks, nor purely end-to-end neural networks. The future lies in the deep integration of classical safety architectures and large-scale AI-driven systems. If you want to work on problems that build the future of autonomous systems over the next decade, this is the place.

What You’ll Be Doing

• Define quality metrics and evaluation methods that reveal where auto-labeling pipelines succeed, fail, or regress.
• Build and maintain human-verified golden datasets covering representative driving conditions, critical corner cases, historical failures, and new requirements.
• Analyze large-scale sensor, camera and annotation data to identify root causes, discover systematic errors, and prioritize improvements with perception and auto-labeling engineers.
• Develop automated approaches for failure clustering, similar-case discovery, and targeted selection of data for human correction.
• Build statistically sound audits and experiments, establish release acceptance criteria, and communicate findings through clear dashboards and recommendations.
• Partner with labeling operations to improve annotation guidelines, reviewer consistency, and the balance between quality, efficiency, and cost.

What We Need To See

• Great foundations in statistics, experimental design, sampling, hypothesis testing, and uncertainty estimation, with practical experience evaluating machine learning systems and data quality.
• Experience working on AI/LLM evaluation with proficiency in Python and SQL, including analyzing large datasets and building reproducible analytical workflows. Understanding of precision, recall, class imbalance, selection bias, data leakage, and evaluation across significant scenario groups.
• Ability to distinguish reference-label defects from model errors and translate ambiguous quality concerns into measurable questions and actionable recommendations.
• Strong communication and teamwork skills, with experience working across engineering, research, product, or operations teams.
• 12+ years of industry experience with Bachelor degree or proven track record for Master in statistics, mathematics, computer science, engineering, or a related quantitative field, or equivalent experience in practice.

Ways To Stand Out from the Crowd:

• Background with autonomous driving, robotics, or computer vision, particularly camera and LiDAR data, 2D/3D annotations, multi-object tracking, occupancy, or temporal and cross-sensor consistency.
• Experience designing human-in-the-loop labeling and audit workflows, measuring annotator agreement, or maintaining versioned benchmarks with protected evaluation sets.

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